3DOR 15

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https://diglib.eg.org/handle/10.2312/12281

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Browsing 3DOR 15 by Subject "I.3.8 [Computer Graphics]"

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    Bag of Compact HKS-based Feature Descriptors
    (The Eurographics Association, 2015) ElNaghy, Hanan; Hamad, Safwat; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    3D object retrieval has become an integral part in many today's applications attracting extensive research efforts. This paper introduces an enhanced 3D object retrieval technique using a compact and highly discriminative feature point descriptor. The key idea is based on integrating Bag of features (BoF) paradigm with Heat Kernel Signature (HKS) for feature description and detection. Initially, HKS computation phase defines HKS point signatures for each 3D model. Then, an innovative feature point detection algorithm provides a succinct set of feature points to be associated with a compact HKS-based descriptor vectors computed at local time scales. Finally, we take advantage of the BoF paradigm to encode a given 3D model with an informative feature frequency vector. The proposed approach has been evaluated on SHREC 2015 dataset of non-rigid models. The experimental results demonstrate the effective retrieval performance, invariance to different kinds of deformation and possible noise.
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    Partial 3D Object Retrieval combining Local Shape Descriptors with Global Fisher Vectors
    (The Eurographics Association, 2015) Savelonas, Michalis A.; Pratikakis, Ioannis; Sfikas, Konstantinos; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    This work introduces a partial 3D object retrieval method, applicable on both meshes and point clouds, which is based on a hybrid shape matching scheme combining local shape descriptors with global Fisher vectors. The differential fast point feature histogram (dFPFH) is defined so as to extend the well-known FPFH descriptor in order to capture local geometry transitions. Local shape similarity is quantified by averaging the minimum weighted distances associated with pairs of dFPFH values calculated on the partial query and the target object. Global shape similarity is derived by means of a weighted distance of Fisher vectors. Local and global distances are derived for multiple scales and are being combined to obtain a ranked list of the most similar complete 3D objects. Experiments on the large-scale benchmark dataset for partial object retrieval of the shape retrieval contest (SHREC) 2013, as well as on the publicly available Hampson pottery dataset, support improved performance of the proposed method against seven recently evaluated retrieval methods.
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    A Spatio-Temporal Descriptor for Dynamic 3D Facial Expression Retrieval and Recognition
    (The Eurographics Association, 2015) Danelakis, Antonios; Theoharis, Theoharis; Pratikakis, Ioannis; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    The recent availability of dynamic 3D facial scans has spawned research activity in recognition based on such data. However, the problem of facial expression retrieval based on dynamic 3D facial data has hardly been addressed and is the subject of this paper. A novel descriptor is created, capturing the spatio-temporal deformation of the 3D facial mesh sequence. Experiments have been implemented using the standard BU - 4DFE dataset. The obtained retrieval results exceed the state-of-the-art results and the new descriptor is much more frugal in terms of space requirements. Furthermore, a methodology which exploits the retrieval results, in order to achieve unsupervised dynamic 3D facial expression recognition is presented, in order to directly compare the proposed descriptor against the wealth of works in recognition. The aforementioned unsupervised methodology outperforms the supervised dynamic 3D facial expression recognition state-of-the-art techniques in terms of classification accuracy.